rrwrite-plan-manuscript

name	rrwrite-plan-manuscript
description	Analyzes the repository structure and generates a detailed manuscript outline based on target journal guidelines (Nature, PLOS, Bioinformatics).
arguments	[{"name":"target_dir","description":"Output directory for manuscript files (e.g., manuscript/repo_v1)","default":"manuscript"}]
allowed-tools	null

Manuscript Planning Protocol

Phase 0.5: Check Repository Analysis Status

Before beginning reconnaissance, check if repository analysis has been performed:

import sys
from pathlib import Path
sys.path.insert(0, str(Path('scripts').resolve()))
from rrwrite_state_manager import StateManager

manager = StateManager(output_dir="{target_dir}", enable_git=False)
state = manager.state

repo_analysis_status = state.get("workflow_status", {}).get("repository_analysis", {}).get("status")

if repo_analysis_status != "completed":
    print("")
    print("=" * 60)
    print("RECOMMENDATION: Run Repository Analysis First")
    print("=" * 60)
    print("")
    print("For best results, analyze your repository before planning:")
    print("  /rrwrite-analyze-repository --repo-path <path> --target-dir {target_dir}")
    print("")

    response = input("Proceed with planning without analysis? [y/N]: ").strip().lower()
    if response not in ['y', 'yes']:
        print("\nExiting. Please run repository analysis first.")
        sys.exit(0)
    else:
        print("\nProceeding with planning without repository analysis...")
else:
    print("✓ Repository analysis found - using existing analysis data")

Phase 1: Repository Reconnaissance

1. Map Structure: Execute tree -L 2 --prune to understand the project layout.
2. Locate Assets:
   • Find all data files (*.csv, *.xlsx) in data/.
   • Find all analysis notebooks (*.ipynb) and scripts (*.py).
   • Find all figures (*.png, *.pdf).
3. Read Context: Read README.md and PROJECT.md to understand the project goals.

Phase 1.5: Load Word Limit Configuration

Before planning sections, load the word limit configuration for the target journal:

python scripts/rrwrite-config-manager.py --journal {journal} --export

This returns:

• Total manuscript word limit
• Per-section word targets (min, target, max)
• Journal-specific formatting requirements

IMPORTANT: Use these limits when specifying word counts in the outline. The default 6000-word total ensures concise, focused manuscripts.

Phase 2: Journal Template Selection

Ask the user for the target journal. Based on the response, adopt the corresponding structure:

Option A: Nature Methods

• Structure: Introduction -> Results -> Discussion -> Methods.
• Focus: Novelty, Comparison to SOTA.
• Constraints: Methods section limited; move extensive details to Supplementary.

Option B: PLOS Computational Biology

• Structure: Abstract -> Author Summary -> Introduction -> Results -> Discussion -> Methods.
• Focus: Reproducibility, Biological Insight.
• Constraints: Mandatory "Author Summary" (non-technical).

Option C: Bioinformatics

• Structure: Abstract -> Intro -> Algorithm -> Implementation -> Discussion.
• Focus: Software utility, Performance benchmarks.

Phase 3: Outline Synthesis

Generate a file named {target_dir}/outline.md (where {target_dir} is the output directory specified when calling this skill, default: manuscript). For each section in the template:

1. Write a Description: What represents the core argument of this section?
2. Link Files: Explicitly list the relative paths of the code/data files that support this section.
   • Example: "Results > Section 2.1: Performance. Supports: results/accuracy_table.csv, figures/fig2_roc.png."
3. Word Count Target: Use the word limits from Phase 1.5 configuration. Include min, target, and max for each section.

Required Structure (per schema: schemas/manuscript.yaml)

The outline MUST include:

• Filename: {target_dir}/outline.md
• Target journal specification
• Sections with:
  • Section name (Abstract, Introduction, Methods, Results, Discussion)
  • Word count targets
  • Evidence files (data, scripts, figures)
  • Key points to cover

Output and Validation

1. Create {target_dir}/outline.md with the structured plan
2. Validate the outline:

   python scripts/rrwrite-validate-manuscript.py --file {target_dir}/outline.md --type outline
   

3. Update workflow state (mark planning stage as completed):

   import sys
   from pathlib import Path
   sys.path.insert(0, str(Path('scripts').resolve()))
   from rrwrite_state_manager import StateManager
   
   manager = StateManager(output_dir="{target_dir}")
   manager.update_workflow_stage("plan", status="completed", file_path="{target_dir}/outline.md")
   

4. Display progress:

   python scripts/rrwrite-status.py --output-dir {target_dir}
   

5. If validation passes, confirm creation and ask user to review
6. If validation fails, fix issues and re-validate

Confirm the creation of {target_dir}/outline.md and validation status. Show the updated workflow status.